


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


A			:						Dict        =  logging.get_logger(__name__)



class 				A       (			_lowerCAmelCase				):
					'''simple docstring'''






					A__     =					['''pixel_values''']

					def __init__(self       :		Any ,   _UpperCAmelCase       :		Any = True ,   _UpperCAmelCase       :		Optional[int] = None ,   _UpperCAmelCase       :		Dict = PIL.Image.BICUBIC ,   _UpperCAmelCase       :		List[str] = True ,   _UpperCAmelCase       :		List[str] = None ,   _UpperCAmelCase       :		Union[str, Any] = 1 / 255 ,   _UpperCAmelCase       :		Tuple = True ,   _UpperCAmelCase       :		Union[str, Any] = True ,   _UpperCAmelCase       :		Any = None ,   _UpperCAmelCase       :		Dict = None ,   **_UpperCAmelCase       :		Optional[int] ,   )		->  None:




							"""simple docstring"""


							super().__init__(**SCREAMING_SNAKE_CASE_						)
							lowercase__	     =						size if size is not None else {"""height""": 256, """width""": 256}
							lowercase__	     =						get_size_dict(SCREAMING_SNAKE_CASE_						)
							lowercase__	     =						crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							lowercase__	     =						get_size_dict(SCREAMING_SNAKE_CASE_ ,   param_name="""crop_size"""						)

							lowercase__	     =						do_resize
							lowercase__	     =						size
							lowercase__	     =						resample
							lowercase__	     =						do_center_crop
							lowercase__	     =						crop_size
							lowercase__	     =						do_rescale
							lowercase__	     =						rescale_factor
							lowercase__	     =						do_normalize
							lowercase__	     =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowercase__	     =						image_std if image_std is not None else IMAGENET_STANDARD_STD

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Dict = PIL.Image.BICUBIC ,   _UpperCAmelCase       :		List[str] = None ,   **_UpperCAmelCase       :		Tuple ,   )		->  np.ndarray:




							"""simple docstring"""


							lowercase__	     =						get_size_dict(SCREAMING_SNAKE_CASE_						)
							if "height" not in size or "width" not in size:
									raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
							return resize(
							    SCREAMING_SNAKE_CASE_ ,   size=(size["""height"""], size["""width"""]) ,   resample=SCREAMING_SNAKE_CASE_ ,   data_format=SCREAMING_SNAKE_CASE_ ,   **SCREAMING_SNAKE_CASE_						)

					def     lowerCamelCase__	(self       :		str ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[Any] = None ,   **_UpperCAmelCase       :		List[Any] ,   )		->  np.ndarray:




							"""simple docstring"""


							lowercase__	     =						get_size_dict(SCREAMING_SNAKE_CASE_						)
							if "height" not in size or "width" not in size:
									raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''						)
							return center_crop(SCREAMING_SNAKE_CASE_ ,   size=(size["""height"""], size["""width"""]) ,   data_format=SCREAMING_SNAKE_CASE_ ,   **SCREAMING_SNAKE_CASE_						)

					def     lowerCamelCase__	(self       :		Dict ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		List[str] = None ,   **_UpperCAmelCase       :		Dict ,   )		->  int:




							"""simple docstring"""


							return rescale(SCREAMING_SNAKE_CASE_ ,   scale=SCREAMING_SNAKE_CASE_ ,   data_format=SCREAMING_SNAKE_CASE_ ,   **SCREAMING_SNAKE_CASE_						)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Dict = None ,   **_UpperCAmelCase       :		str ,   )		->  np.ndarray:




							"""simple docstring"""


							return normalize(SCREAMING_SNAKE_CASE_ ,   mean=SCREAMING_SNAKE_CASE_ ,   std=SCREAMING_SNAKE_CASE_ ,   data_format=SCREAMING_SNAKE_CASE_ ,   **SCREAMING_SNAKE_CASE_						)


					def     lowerCamelCase__	(self       :		Optional[int] ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Tuple = None ,   _UpperCAmelCase       :		Optional[int] = None ,   _UpperCAmelCase       :		Optional[int]=None ,   _UpperCAmelCase       :		Any = None ,   _UpperCAmelCase       :		List[str] = None ,   _UpperCAmelCase       :		Tuple = None ,   _UpperCAmelCase       :		List[str] = None ,   _UpperCAmelCase       :		Any = None ,   _UpperCAmelCase       :		Optional[int] = None ,   _UpperCAmelCase       :		Optional[Any] = None ,   _UpperCAmelCase       :		Any = None ,   _UpperCAmelCase       :		Optional[int] = ChannelDimension.FIRST ,   **_UpperCAmelCase       :		Any ,   )		->  PIL.Image.Image:




							"""simple docstring"""


							lowercase__	     =						do_resize if do_resize is not None else self.do_resize
							lowercase__	     =						resample if resample is not None else self.resample
							lowercase__	     =						do_center_crop if do_center_crop is not None else self.do_center_crop
							lowercase__	     =						do_rescale if do_rescale is not None else self.do_rescale
							lowercase__	     =						rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase__	     =						do_normalize if do_normalize is not None else self.do_normalize
							lowercase__	     =						image_mean if image_mean is not None else self.image_mean
							lowercase__	     =						image_std if image_std is not None else self.image_std

							lowercase__	     =						size if size is not None else self.size
							lowercase__	     =						get_size_dict(SCREAMING_SNAKE_CASE_						)
							lowercase__	     =						crop_size if crop_size is not None else self.crop_size
							lowercase__	     =						get_size_dict(SCREAMING_SNAKE_CASE_ ,   param_name="""crop_size"""						)

							lowercase__	     =						make_list_of_images(SCREAMING_SNAKE_CASE_						)

							if not valid_images(SCREAMING_SNAKE_CASE_						):
									raise ValueError(
									    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
									    """torch.Tensor, tf.Tensor or jax.ndarray."""						)

							if do_resize and size is None or resample is None:
									raise ValueError("""Size and resample must be specified if do_resize is True."""						)

							if do_center_crop and crop_size is None:
									raise ValueError("""Crop size must be specified if do_center_crop is True."""						)

							if do_rescale and rescale_factor is None:
									raise ValueError("""Rescale factor must be specified if do_rescale is True."""						)

							if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("""Image mean and std must be specified if do_normalize is True."""						)

							# All transformations expect numpy arrays.
							lowercase__	     =						[to_numpy_array(SCREAMING_SNAKE_CASE_						) for image in images]

							if do_resize:
									lowercase__	     =						[self.resize(image=SCREAMING_SNAKE_CASE_ ,   size=SCREAMING_SNAKE_CASE_ ,   resample=SCREAMING_SNAKE_CASE_						) for image in images]

							if do_center_crop:
									lowercase__	     =						[self.center_crop(image=SCREAMING_SNAKE_CASE_ ,   size=SCREAMING_SNAKE_CASE_						) for image in images]

							if do_rescale:
									lowercase__	     =						[self.rescale(image=SCREAMING_SNAKE_CASE_ ,   scale=SCREAMING_SNAKE_CASE_						) for image in images]

							if do_normalize:
									lowercase__	     =						[self.normalize(image=SCREAMING_SNAKE_CASE_ ,   mean=SCREAMING_SNAKE_CASE_ ,   std=SCREAMING_SNAKE_CASE_						) for image in images]

							lowercase__	     =						[to_channel_dimension_format(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_						) for image in images]

							lowercase__	     =						{"""pixel_values""": images}
							return BatchFeature(data=SCREAMING_SNAKE_CASE_ ,   tensor_type=SCREAMING_SNAKE_CASE_						)






#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist







def  lowerCAmelCase_	(*lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			with open(lowerCAmelCase__		,						"""r"""  ) as fh:
						fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_EX  )
						try:
									print(*lowerCAmelCase__  )
						finally:
									fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_UN  )


a      :  Tuple			     =			int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
a      :  Union[str, Any]			     =			torch.device('cuda', local_rank)
a      :  Any			     =			socket.gethostname()

a      :  Optional[Any]			     =			F'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	a      :  Optional[Any]			     =			dist.get_rank()
	a      :  List[Any]			     =			dist.get_world_size()

	printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(F'''{gpu} is broken''')
	raise






